Kun Chen is a research fellow at the Center for Computational Quantum Physics (CCQ) at the Flatiron Institute. He leads a team that develops Julia packages under the Numerical Effective Field Theory (NEFT) framework (https://github.com/numericalEFT) for modeling real-world quantum materials using modern quantum field theory. These efforts have resulted in powerful tools such as MCIntegration.jl for high-dimensional Monte Carlo integration, Lehmann.jl for low-rank approximation of Green's function, and GreenFunc.jl for studying quantum many-body physics. With a PhD in 2018 from the University of Massachusetts Amherst, Kun is a Simons Postdoctoral Fellow at Rutgers University and later a research fellow at Flatiron Institute. His work in this area will enable scientists and researchers to gain a deeper understanding of quantum materials and their properties.
This talk presents a new Julia package for efficient and generic Monte Carlo integration in high-dimensional and complex domains, featuring the Vegas algorithm for self-adaptive important sampling and an improved algorithm for increased robustness. The package demonstrates Julia's superiority over C/C++/Fortran and Python for high-dimensional Monte Carlo integration by enabling the easy creation of user-defined integrand evaluation functions with the speed of C and the flexibility of Python.